摘要
准确的电力异常用户识别方法能为供电企业锁定存在窃电行为或其他违规行为的电力用户提供参考。大多数基于机器学习的异常识别模型采用了无监督算法,但模型的准确度还较低。针对上述问题,提出一种结合无监督的局部离群因子(LOF)算法与有监督的支持向量机(SVM)算法的两阶段异常用电用户识别方法。基于分析异常电能表区别于正常电能表的电流电压表现,构建异常识别模型的输入特征;采用无监督的LOF算法进行采样,筛选出可疑样本交给人工进行标记,然后利用标记样本训练有监督的SVM模型;在之后的检测工作中,直接将LOF算法筛选出可疑样本交给SVM模型进行识别。实例结果表明,该方法对电力异常用户的识别准确度高,对供电企业的窃电稽查工作具有指导意义。
Accurate identification method of abnormal electricity users can provide reference for power supply enterprises to lock in electricity theft or other violations of power users. Most abnormal user identification models based on machine learning adopt unsupervised algorithms,but the accuracy of the models is low. To solve the above problems,a two-stage abnormal power user identification method combining unsupervised local outlier factor(LOF)algorithm and supervised support vector machine(SVM)algorithm was proposed. Based on the analysis of the current and voltage performance of the abnormal energy meter different from the normal energy meter,the input characteristics of the abnormal identification model were constructed. The LOF algorithm was used to sample,and the suspicious samples were selected and handed over to manual labeling. Then the supervised SVM model was trained by the labeled samples. In the subsequent detection work,the suspicious samples screened by LOF algorithm were directly sent to the SVM model for identification. The example results show that this method has high identification accuracy for power abnormal users,and has guiding significance for the power stealing inspection of power supply enterprises.
作者
顾臻
庄葛巍
贺青
周磊
安佰龙
段艳
GU Zhen;ZHUANG Gewei;HE Qing;ZHOU Lei;AN Bailong;DUAN Yan(Power Science Research Institute,State Grid Shanghai Electric Power Company,Shanghai 200051,China;Department of Vehicle Engineering,School of Automobile,Tongji University,Shanghai 201804,China)
出处
《电气传动》
2023年第3期90-96,共7页
Electric Drive
